Lidar fault detection system

ABSTRACT

Aspects of the present disclosure involve systems, methods, and devices for fault detection in a Lidar system. A fault detection system obtains incoming Lidar data output by a Lidar system during operation of an AV system. The incoming Lidar data includes one or more data points corresponding to a fault detection target on an exterior of a vehicle of the AV system. The fault detection system accesses historical Lidar data that is based on data previously output by the Lidar system. The historical Lidar data corresponds to the fault detection target. The fault detection system performs a comparison of the incoming Lidar data with the historical Lidar data to identify any differences between the two sets of data. The fault detection system detects a fault condition occurring at the Lidar system based on the comparison.

CLAIM FOR PRIORITY

This application claims the benefit of priority of U.S. ProvisionalApplication No. 62/870,183, filed Jul. 3, 2019, which is herebyincorporated by reference in its entirety.

TECHNICAL FIELD

The subject matter disclosed herein relates to light detection andranging (Lidar) systems. In particular, example embodiments relate tosystems and methods for fault detection in Lidar systems.

BACKGROUND

Lidar is a radar-like system that uses lasers to createthree-dimensional representations of surrounding environments. A Lidarunit includes at least one laser emitter paired with a detector to forma channel, though an array of channels may be used to expand the fieldof view of the Lidar unit. During operation, each channel emits a lasersignal into the environment that is reflected off of the surroundingenvironment back to the detector. A single channel provides a singlepoint of ranging information. Collectively, channels are combined tocreate a point cloud that corresponds to a three-dimensionalrepresentation of the surrounding environment. The Lidar unit alsoincludes circuitry to measure the time of flight - i.e., the elapsedtime from emitting the laser signal to detecting the return signal. Thetime of flight is used to determine the distance of the Lidar unit tothe detected object.

BRIEF DESCRIPTION OF THE DRAWINGS

Various ones of the appended drawings merely illustrate exampleembodiments of the present inventive subject matter and cannot beconsidered as limiting its scope.

FIG. 1 is a block diagram illustrating an example autonomous vehicle(AV) system in which a fault detection system may be deployed, accordingto some embodiments.

FIG. 2 is a block diagram illustrating a Lidar unit, which may beincluded as part of the AV system and in communication with the faultdetection system, according to some embodiments.

FIGS. 3-6 are flowcharts illustrating example operations performed aspart of a method for fault detection in a Lidar system, according tosome embodiments.

FIG. 7 is a diagrammatic representation of a machine in the example formof a computer system within which a set of instructions for causing themachine to perform any one or more of the methodologies discussed hereinmay be executed.

DETAILED DESCRIPTION

Reference will now be made in detail to specific example embodiments forcarrying out the inventive subject matter. Examples of these specificembodiments are illustrated in the accompanying drawings, and specificdetails are set forth in the following description in order to provide athorough understanding of the subject matter. It will be understood thatthese examples are not intended to limit the scope of the claims to theillustrated embodiments. On the contrary, they are intended to coversuch alternatives, modifications, and equivalents as may be includedwithin the scope of the disclosure.

Increasingly, Lidar is finding applications in autonomous vehicles (AVs)such as partially or fully autonomous cars. For example, data providedby Lidar systems is frequently used in the localization, perception,prediction, and motion planning of AVs because this data providesimportant information that describes the surrounding environment. Duringan operating AV’s lifetime, optical components of its Lidar system maybecome dislodged, damaged, dirty, or otherwise occluded, and theintensity of laser signals provided by laser emitters of the Lidarsystem may be reduced, or detectors of the Lidar system may become lesssensitive. All of these fault conditions reduce the ability of the Lidarsystem to sense the external world and provide accurate data to thecomponents of the AV system, which severely impacts the AV system’sability to perform localization, perception, prediction, and motionplanning operations. In general, any circumstance that impacts the Lidarsystem’s ability to sense the surrounding environment can be problematicfor proper operation of the AV system. However, given that thesurrounding environment is constantly changing during the operation ofthe AV system, it can be difficult to determine when the Lidar system’sability to sense the surrounding environment is impaired because theremay be no indication of the underlying fault conditions that lead to theimpairment.

Aspects of the present disclosure address these foregoing issues, amongothers, with systems, methods, and devices for fault detection in Lidarsystems. A method for fault detection includes comparing incoming Lidardata with historical Lidar data to determine whether the Lidar system iscurrently operating as well as it was at a previous time. To overcomechallenges presented by ever-changing surrounding environments, a faultdetection target on an exterior of the AV is relied upon in faultdetection to provide a standard of comparison when comparing currentincoming Lidar data with historical Lidar data. The fault detectiontarget may comprise one or more fixed features of the exterior of the AVor may comprise one or more separate components that are affixedthereto.

Consistent with some embodiments, a fault detection system obtainsincoming Lidar data output by a Lidar system during operation of an AVsystem. The incoming Lidar data includes one or more data pointscorresponding to the fault detection target. That is, the one or moredata points are based on one or more return signals resulting fromemitted light signals reflecting off the fault detection target. Thefault detection system accesses historical Lidar data that is based ondata previously output by the Lidar system. The historical Lidar datacorresponds to the fault detection target. The historical Lidar data mayinclude raw data or average values based on multiple previous passesover the fault detection target. In some embodiments, the historicalLidar data may include one or more algorithms based on raw Lidar data(e.g., artificial intelligence (AI) algorithms trained on raw Lidardata).

The fault detection system performs a comparison of the incoming Lidardata with the historical Lidar data to identify any differences betweenthe two sets of data. The fault detection system may detect a faultcondition occurring at the Lidar system based on the comparison. Forexample, the fault detection system may determine that a differencebetween the incoming Lidar data and the historical Lidar data satisfiesa threshold condition. Based on detecting the fault condition, the faultdetection system initiates a fail-safe state for the AV system. Theinitiating of the fail-safe state may result in one or more operationsof the AV system being restricted until a remediation action (e.g.,replacing, repairing, or cleaning a component of the AV system) isperformed to avoid any potential issues that may occur as a result ofthe Lidar system providing potentially inaccurate information todownstream components of the AV system.

With reference to FIG. 1 , an example autonomous vehicle (AV) system 100is illustrated, according to some embodiments. To avoid obscuring theinventive subject matter with unnecessary detail, various functionalcomponents that are not germane to conveying an understanding of theinventive subject matter have been omitted from FIG. 1 . However, askilled artisan will readily recognize that various additionalfunctional components may be included as part of the AV system 100 tofacilitate additional functionality that is not specifically describedherein.

The AV system 100 is responsible for controlling a vehicle. The AVsystem 100 is capable of sensing its environment and navigating withouthuman input. The AV system 100 can include a ground-based AV (e.g., car,truck, bus), an air-based AV (e.g., airplane, drone, helicopter, orother aircraft), or other types of vehicles (e.g., watercraft).

The AV system 100 includes a vehicle computing system 102, one or moresensors 104, and one or more vehicle controls 116. The vehicle computingsystem 102 can assist in controlling the AV system 100. In particular,the vehicle computing system 102 can receive sensor data from the one ormore sensors 104, attempt to comprehend the surrounding environment byperforming various processing techniques on data collected by thesensors 104, and generate an appropriate motion path through such asurrounding environment. The vehicle computing system 102 can controlthe one or more vehicle controls 116 to operate the AV system 100according to the motion path.

As illustrated in FIG. 1 , the vehicle computing system 102 can includeone or more computing devices that assist in controlling the AV system100. The vehicle computing system 102 can include a localizer system106, a perception system 108, a prediction system 110, a motion planningsystem 112, and a fault detection system 120 that cooperate to perceivethe dynamic surrounding environment of the AV system 100 and determine atrajectory describing a proposed motion path for the AV system 100. Thevehicle computing system 102 can additionally include a vehiclecontroller 114 configured to control the one or more vehicle controls116 (e.g., actuators that control gas flow (propulsion), steering,braking) to execute the motion of the AV system 100 to follow thetrajectory.

In particular, in some implementations, any one of the localizer system106, the perception system 108, the prediction system 110, the motionplanning system 112, or the fault detection system 120 can receivesensor data from the one or more sensors 104 that are coupled to orotherwise included within the AV system 100. As examples, the one ormore sensors 104 can include a Lidar system 118, a Radio Detection andRanging (RADAR) system, one or more cameras (e.g., visible spectrumcameras, infrared cameras), and/or other sensors. The sensor data caninclude information that describes the location of objects within thesurrounding environment of the AV system 100.

As one example, for the Lidar system 118, the sensor data can includepoint data (also referred to herein as “Lidar data”) that includes thelocation (e.g., in three-dimensional space relative to the Lidar system118) of a number of points that correspond to objects that havereflected an emitted laser. For example, the Lidar system 118 canmeasure distances by measuring the time of flight (ToF) that it takes ashort laser pulse to travel from the sensor(s) 104 to an object andback, calculating the distance from the known speed of light. The pointdata further includes an intensity value for each point, which, asdescribed above, can provide information about the reflectiveness of theobjects that have reflected the emitted laser.

As another example, for RADAR systems, the sensor data can include thelocation (e.g., in three-dimensional space relative to the RADAR system)of a number of points that correspond to objects that have reflected aranging radio wave. For example, radio waves (e.g., pulsed orcontinuous) transmitted by the RADAR system can reflect off an objectand return to a receiver of the RADAR system, giving information aboutthe object’s location and speed. Thus, a RADAR system can provide usefulinformation about the current speed of an object.

As yet another example, for cameras, various processing techniques(e.g., range imaging techniques such as, for example, structure frommotion, structured light, stereo triangulation, and/or other techniques)can be performed to identify the location (e.g., in three-dimensionalspace relative to a camera) of a number of points that correspond toobjects that are depicted in imagery captured by the camera. Othersensor systems can identify the location of points that correspond toobjects as well.

As another example, the one or more sensors 104 can include apositioning system 124. The positioning system 124 can determine acurrent position of the AV system 100. The positioning system 124 can beany device or circuitry for analyzing the position of the AV system 100.For example, the positioning system 124 can determine position by usinginertial sensors; by using a satellite positioning system; based onInternet Protocol (IP) address; by using triangulation and/or proximityto network access points or other network components (e.g., cellulartowers, Wi-Fi access points); and/or by other suitable techniques. Theposition of the AV system 100 can be used by various systems of thevehicle computing system 102.

Thus, the one or more sensors 104 can be used to collect sensor datathat includes information that describes the location (e.g., inthree-dimensional space relative to the AV system 100) of points thatcorrespond to objects within the surrounding environment of the AVsystem 100.

In addition to the sensor data, the localizer system 106, the perceptionsystem 108, the prediction system 110, the motion planning system 112,and/or the fault detection system 120 can retrieve or otherwise obtainmap data 122 that provides detailed information about the surroundingenvironment of the AV system 100. The map data 122 can provideinformation regarding the identity and location of different travelways(e.g., roadways, alleyways, trails, and other paths designated fortravel), road segments, buildings, or other items or objects (e.g.,lampposts, crosswalks, curbing); known reflectiveness (e.g., radiance)of different travelways (e.g., roadways), road segments, buildings, orother items or objects (e.g., lampposts, crosswalks, curbing); thelocation and directions of traffic lanes (e.g., the location anddirection of a parking lane, a turning lane, a bicycle lane, or otherlanes within a particular roadway or other travelway); traffic controldata (e.g., the location and instructions of signage, traffic lights, orother traffic control devices); and/or any other map data that providesinformation that assists the vehicle computing system 102 incomprehending and perceiving its surrounding environment and itsrelationship thereto.

The localizer system 106 receives the map data 122 and some or all ofthe sensor data from the sensors 104, and generates vehicle poses forthe AV system 100 based on this information. A vehicle pose describesthe position and orientation of the vehicle. The position of the AVsystem 100 is a point in a three-dimensional space. In some examples,the position is described by values for a set of Cartesian coordinates,although any other suitable coordinate system may be used. In someexamples, the vehicle orientation is described by a yaw about thevertical axis, a pitch about a first horizontal axis, and a roll about asecond horizontal axis. In some examples, the localizer system 106generates vehicle poses periodically (e.g., every second, every halfsecond). The localizer system 106 appends time stamps to vehicle poses,where the time stamp for a pose indicates the point in time that isdescribed by the pose. The localizer system 106 generates vehicle posesby comparing sensor data (e.g., remote sensor data) to the map data 122describing the surrounding environment of the AV system 100.

In some examples, the localizer system 106 includes one or morelocalizers and a pose filter. Localizers generate pose estimates bycomparing remote sensor data (e.g., Lidar data, RADAR data) to the mapdata 122. The pose filter receives pose estimates from the one or morelocalizers as well as other sensor data such as, for example, motionsensor data from an inertial measurement unit (IMU), encoder, odometer,and the like. In some examples, the pose filter executes a Kalman filteror other machine learning algorithm to combine pose estimates from theone or more localizers with motion sensor data to generate vehicleposes.

The perception system 108 can identify one or more objects that areproximate to the AV system 100 based on sensor data received from theone or more sensors 104, inferred reflectance values provided by thefault detection system 120, and/or the map data 122. In particular, insome implementations, the perception system 108 can determine, for eachobject, state data that describes a current state of the object. Asexamples, the state data for each object can describe an estimate of theobject’s current location (also referred to as position), current speed(also referred to as velocity), current acceleration, current heading,current orientation, size/footprint (e.g., as represented by a boundingshape such as a bounding polygon or polyhedron), class (e.g., vehicle,pedestrian, bicycle, or other), yaw rate, reflectance characteristics,specular or diffuse reflectivity characteristics, and/or other stateinformation.

In some implementations, the perception system 108 can determine statedata for each object over a number of iterations. In particular, theperception system 108 can update the state data for each object at eachiteration. Thus, the perception system 108 can detect and track objects(e.g., vehicles) that are proximate to the AV system 100 over time. Insome instances, the perception system 108 updates state data for anobject based on a diffuse reflectivity value of the object computed bythe fault detection system 120.

The prediction system 110 can receive the state data from the perceptionsystem 108 and predict one or more future locations for each objectbased on such state data. For example, the prediction system 110 canpredict where each object will be located within the next 5 seconds, 10seconds, 20 seconds, and so forth. As one example, an object can bepredicted to adhere to its current trajectory according to its currentspeed. As another example, other, more sophisticated predictiontechniques or modeling can be used.

The motion planning system 112 can determine a motion plan for the AVsystem 100 based at least in part on the predicted one or more futurelocations for the objects provided by the prediction system 110 and/orthe state data for the objects provided by the perception system 108.Stated differently, given information about the current locations ofobjects and/or predicted future locations of proximate objects, themotion planning system 112 can determine a motion plan for the AV system100 that best navigates the AV system 100 relative to the objects atsuch locations.

The motion plan can be provided by the motion planning system 112 to thevehicle controller 114. In some implementations, the vehicle controller114 can be a linear controller that may not have the same level ofinformation about the environment and obstacles around the desired pathof movement as is available in other computing system components (e.g.,the perception system 108, prediction system 110, motion planning system112). Nonetheless, the vehicle controller 114 can function to keep theAV system 100 reasonably close to the motion plan.

More particularly, the vehicle controller 114 can be configured tocontrol motion of the AV system 100 to follow the motion plan. Thevehicle controller 114 can control one or more of propulsion and brakingof the AV system 100 to follow the motion plan. The vehicle controller114 can also control steering of the AV system 100 to follow the motionplan. In some implementations, the vehicle controller 114 can beconfigured to generate one or more vehicle actuator commands and tofurther control one or more vehicle actuators provided within thevehicle controls 116 in accordance with the vehicle actuator command(s).Vehicle actuators within the vehicle controls 116 can include, forexample, a steering actuator, a braking actuator, and/or a propulsionactuator.

The fault detection system 120 is responsible for detecting faultconditions occurring at the Lidar system 118. The fault conditions may,for example, comprise or correspond to one or more of a damaged, dirty,dislodged, fouled, or otherwise occluded optical component (e.g., a lensof the Lidar system 118), a reduced or diminished intensity of lasersignals emitted by one or more emitting lasers of the Lidar system 118,or a reduced sensitivity of one of the detectors in the Lidar system118.

During operation of the AV system 100, the fault detection system 120compares incoming Lidar data output by the Lidar system 118 withhistorical Lidar data generated based on data previously output by theLidar system 118. In particular, the fault detection system 120 comparesincoming Lidar data corresponding to a fault detection target 126 withhistorical Lidar data corresponding to the fault detection target 126.In some embodiments, the historical Lidar data may be stored in andaccessed from a memory of the vehicle computing system 102. In someembodiments, the historical Lidar data may be stored and accessed from amemory in the Lidar system 118 or a memory in the fault detection system120. In some embodiments, the fault detection system 120 along with thestored historical data may be implemented within the Lidar system 118.

The fault detection system 120 may detect one or more fault conditionsbased on the comparison of the incoming Lidar data with the historicalLidar data. For example, the fault detection system 120 may identify adifference between the incoming and historical Lidar data that indicatesthat a fault condition is occurring. In response to detecting the faultcondition, the fault detection system 120 initiates a fail-safe statefor the AV system 100. For example, the fault detection system 120 mayissue one or more commands to the vehicle controller 114 to initiate thefail-safe state. The initiating of the fail-safe state may cause one ormore operations of the AV system 100 to be restricted.

By relying specifically on Lidar data corresponding to the faultdetection target 126, the fault detection system 120 ensures a standardof comparison for comparing the incoming Lidar data with the historicalLidar data, given that the fault detection target 126 is fixed ratherthan constantly changing like the surrounding environment. The faultdetection target 126 may comprise one or more fixed features of anexterior of the AV, a separate component that is affixed thereto, or acombination thereof. For example, the fault detection target 126 maycomprise a surface coating (e.g., a paint strip) applied to the exteriorof the vehicle. As another example, the fault detection target 126 maycomprise an existing antenna (e.g., a shark fin antenna) on the vehicle.

The fault detection target 126 may comprise one or more markings. Forexample, the fault detection target 126 may comprise one or moregeometric shapes, and the fault detection target 126 or portions thereofmay comprise one or more patterns.

At least one region of the fault detection target 126 may have areflectivity or color that is different from the reflectivity or colorof the exterior surface of the vehicle, such that it can be easilydistinguished from the vehicle’s exterior by the Lidar system 118.Moreover, the fault detection target 126 may have varying reflectivityor color. For example, a first region of the fault detection target 126may have a first reflectivity and/or color and a second region of thefault detection target 126 may have a second reflectivity and/or color.

The fault detection target 126 may be selected or positioned on thevehicle to maximize visibility by the Lidar system 118 while alsolimiting the obstruction to the field of view of the Lidar system 118and other sensors 104. For example, the fault detection target 126 maybe positioned at the back of the vehicle, given that the vehicle istypically moving forward. However, the position and arrangement of thefault detection target 126 may vary between vehicle models and Lidarsystems. In some embodiments, the fault detection target 126 may have araised component to ensure visibility by each channel of the Lidarsystem 118. The raised component may include one or more additionalmarkings.

In some embodiments, the raised component is stationary, while in otherembodiments the raised component may be an actuatable component that maybe actuated into the field of view of the Lidar system 118 while the AVsystem 100 is stationary and actuated out of the field of view of theLidar system 118 to avoid obstructing the view of the Lidar system 118or other sensors 104 during operation of the vehicle. Consistent withthese embodiments, the fault detection system 120 may actuate thecomponent of the fault detection target 126 to raise and lower it. Forexample, the fault detection system 120 may raise the component of thefault detection target 126 in response to detecting a vehicle stop andmay lower this component in response to determining that the vehicle isonce again in motion.

In some embodiments, the fault detection system 120 may perform thefault condition analysis on a continuous basis, such that the faultdetection system 120 evaluates the incoming Lidar data at each pass todetect fault conditions. In some embodiments, the fault detection system120 may perform the fault condition analysis at periodic time intervals(e.g., every 30 seconds). In some embodiments, the fault conditionanalysis may be programmatically triggered. In some embodiments, thefault condition analysis may be triggered by certain external events oroperations of the AV system 100. For example, the fault conditionanalysis may be triggered at each stop of the vehicle. Triggering thefault condition analysis at each stop may be especially useful inembodiments in which the fault detection target 126 comprises anactuatable component that is raised and lowered so as to avoidobstructing the field of view of the Lidar system 118 during movement ofthe vehicle.

Each of the localizer system 106, the perception system 108, theprediction system 110, the motion planning system 112, the faultdetection system 120, and the vehicle controller 114 can includecomputer logic utilized to provide desired functionality. In someimplementations, each of the localizer system 106, the perception system108, the prediction system 110, the motion planning system 112, thefault detection system 120, and the vehicle controller 114 can beimplemented in hardware, firmware, and/or software controlling ageneral-purpose processor. For example, in some implementations, each ofthe localizer system 106, the perception system 108, the predictionsystem 110, the motion planning system 112, the fault detection system120, and the vehicle controller 114 includes program files stored on astorage device, loaded into a memory, and executed by one or moreprocessors. In other implementations, each of the localizer system 106,the perception system 108, the prediction system 110, the motionplanning system 112, the fault detection system 120, and the vehiclecontroller 114 includes one or more sets of computer-executableinstructions that are stored in a tangible computer-readable storagemedium such as random-access memory (RAM), a hard disk, or optical ormagnetic media.

FIG. 2 is a block diagram illustrating the Lidar system 118, which maybe included as part of the AV system 100, according to some embodiments.To avoid obscuring the inventive subject matter with unnecessary detail,various functional components that are not germane to conveying anunderstanding of the inventive subject matter have been omitted fromFIG. 2 . However, a skilled artisan will readily recognize that variousadditional functional components may be included as part of the Lidarsystem 118 to facilitate additional functionality that is notspecifically described herein.

As shown, the Lidar system 118 comprises channels 200-0 to 200-N; thus,the Lidar system 118 comprises channels 0 to N. Each of the channels200-0 to 200-N outputs point data that provides a single point ofranging information. Collectively, the point data output by each of thechannels 200-0 to 200-N (i.e., point data_(0-N)) is combined to create apoint cloud that corresponds to a three-dimensional representation ofthe surrounding environment.

Each of the channels 200-0 to 200-N comprises an emitter 202 (e.g., alaser emitter) paired with a detector 204. The emitter 202 emits a lasersignal into the environment that is reflected off the surroundingenvironment and returned back to a sensor 206 (e.g., an opticaldetector) in the detector 204. For example, the emitter 202 may emit alaser signal into the environment that is reflected off the faultdetection target 126.

The sensor 206 provides the return signal to a read-out circuit 208, andthe read-out circuit 208, in turn, outputs the point data based on thereturn signal. The point data comprises a distance of the Lidar system118 from a detected surface (e.g., a road) that is determined by theread-out circuit 208 by measuring the ToF, which is the time elapsedbetween the emitter 202 emitting the laser signal and the detector 204detecting the return signal.

The point data further includes an intensity value corresponding to eachreturn signal. The intensity value indicates a measure of intensity ofthe return signal determined by the read-out circuit 208. As notedabove, the intensity of the return signal provides information about thesurface reflecting the signal and can be used by any one of thelocalizer system 106, perception system 108, prediction system 110, andmotion planning system 112 for localization, perception, prediction, andmotion planning.

As shown, point data (i.e., point data_(0-N)) output by the channels200-0 to 200-N of the Lidar system 118 is obtained by the faultdetection system 120 to determine whether a fault condition is occurringat the Lidar system 118. As will be discussed in further detail below,the fault detection system 120 specifically analyzes point datacorresponding to the fault detection target 126. That is, the point dataanalyzed by the fault detection system 120 includes data pointscorresponding to return signals resulting from emitted laser signalsthat have reflected off the fault detection target 126 so as to providea standard for comparison with previous data. Hence, the fault detectionsystem 120 may analyze only a subset of all point data output by thechannels 200-0 to 200-N. Accordingly, the fault detection system 120 mayidentify and isolate the data points output by the Lidar system 118 thatcorrespond to the fault detection target 126.

FIGS. 3-6 are flowcharts illustrating example operations performed aspart of a method 300 for fault detection in a Lidar system, according tosome embodiments. Any one or more of the operations of the method 300may be embodied in computer-readable instructions for execution by ahardware component (e.g., a processor) such that these operations may beperformed by one or more components of the AV system 100. Accordingly,the method 300 is described below, by way of example, with referencethereto. However, it shall be appreciated that the method 300 may bedeployed on various other hardware configurations and is not intended tobe limited to deployment on the AV system 100.

At operation 305, the fault detection system 120 obtains incoming Lidardata output by the Lidar system 118. The incoming Lidar data correspondsto the fault detection target 126. For example, the incoming Lidar datacomprises a set of data points output by the Lidar system 118. The setof data points may include one or more data points output by each of thechannels 200-0 to 200-N. Each data point comprises a raw intensity valueand a range value. The raw intensity value includes a measure ofintensity of a return signal, and the range value includes a measure ofdistance from the Lidar system 118 to a surface or object that reflectedan emitted light signal. In particular, in the incoming Lidar dataobtained at operation 305, each data point corresponds to a returnsignal resulting from an emitted light signal reflecting off of thefault detection target 126.

Consistent with some embodiments, the incoming Lidar data obtained atoperation 305 corresponds to a subset of data points output by the Lidarsystem 118, where other data points output by the Lidar system 118correspond to objects and surfaces in the surrounding environment otherthan the fault detection target 126. Accordingly, the fault detectionsystem 120 may identify and isolate the data points output by the Lidarsystem 118 that correspond to the fault detection target 126.

At operation 310, the fault detection system 120 accesses historicalLidar data based on data points previously output by the Lidar system118. The fault detection system 120 may access the historical Lidar datafrom a memory of the vehicle computing system 102. The historical Lidardata corresponds to the fault detection target 126. In particular, thehistorical Lidar data includes data points generated based on returnsignals resulting from emitted light signals reflecting off the faultdetection target 126. In some embodiments, the historical Lidar dataincludes raw data from one or more passes over the fault detectiontarget 126. That is, for a given point on the fault detection target126, the historical Lidar data includes at least one raw range value andat least one raw intensity value. In some embodiments, the historicalLidar data includes an average of Lidar data from multiple prior passesover the fault detection target 126. That is, for a given point on thefault detection target 126, the historical Lidar data includes anaverage intensity value and an average range value. In some embodiments,the historical Lidar data may include one or more algorithms based onraw Lidar data such as and AI algorithm trained on raw Lidar data).

At operation 315, the fault detection system 120 performs a comparisonof the incoming Lidar data with the historical Lidar data. As will bediscussed in further detail below, the comparison includes determining adifference between the historical Lidar data and the incoming Lidardata.

At operation 320, the fault detection system 120 detects a faultcondition based on the comparison. In some instances, the detected faultcondition is detected at the Lidar system 118. For example, the faultdetection system 120 may determine that a difference between thehistorical Lidar data and the incoming Lidar data satisfies a thresholdcondition, thereby signaling a fault condition. In many instances, thefault condition occurs at the Lidar system 118. In these instances, thefault condition may, for example, comprise or correspond to a damaged,dirty, or dislodged optical component (e.g., a lens of one or moresensors 206), a reduced or diminished intensity of laser signals emittedby one or more emitters 202, or a reduced sensitivity of one or moredetectors 204. As will be discussed below, in other instances, the faultdetection system 120 may detect a fault condition arising based on amisalignment of one or more of the sensors 104 (e.g., an image sensor).

At operation 325, the fault detection system 120 initiates a fail-safestate for the AV system 100. The initiating of the fail-safe state mayinclude restricting or preventing one or more operations of the AVsystem 100. The fault detection system 120 may initiate the fail-safestate by providing one or more commands to the vehicle controller 114and/or one or more components of the vehicle computing system 102. Inresponse to the fault detection system 120 initiating the fail-safestate, the vehicle controller 114 may control operation of the AV suchthat the AV stops at a safe and available stopping location.

As shown in FIG. 4 , the method 300 may, in some embodiments, includeoperations 405, 410, 415, and 420. Consistent with these embodiments,the operation 405 may be performed as part of operation 315, where thefault detection system 120 performs a comparison of the historical Lidardata and the incoming Lidar data.

At operation 405, the fault detection system 120 determines a differencebetween the incoming Lidar data and the historical Lidar data. Indetermining the difference between the incoming Lidar data and thehistorical Lidar data, the fault detection system 120 compares incomingdata points from each of the channels 200-0 to 200-N with correspondinghistorical data points based on data previously generated by each of thechannels 200-0 to 200-N. In performing this comparison, the faultdetection system 120 compares an incoming data point corresponding to aparticular location on the fault detection target 126 with a historicaldata point corresponding to the same location on the fault detectiontarget 126. The fault detection system 120 may identify a difference inrange values, intensity values, or both.

As an example, the fault detection system 120 may determine a differencebetween an incoming intensity value produced by channel 200-1 and acorresponding historical intensity value corresponding to datapreviously produced by the channel 200-1. As another example, the faultdetection system 120 may determine a difference between an incomingrange value produced by the channel 200-1 and a corresponding historicalrange value corresponding to data previously produced by the channel200-1.

In some embodiments, the historical Lidar data comprises raw datapoints. Consistent with these embodiments, the fault detection system120 determines a difference between a raw incoming data point value(e.g., a raw incoming intensity value or raw incoming range value) and araw historical data point value (e.g., a raw historical intensity valueor raw historical range value).

In some embodiments, the historical Lidar data includes one or moreaverage data point values. Consistent with these embodiments, the faultdetection system 120 determines a difference between a raw incoming datapoint value (e.g., a raw incoming intensity value or raw incoming rangevalue) and an average historical data point value (e.g., an averagehistorical intensity value or average historical range value).Accordingly, in performing the comparison of the incoming Lidar datawith the historical Lidar data, the fault detection system 120 maydetermine a historical average intensity value, a historical averagerange value, or both. In some embodiments, the fault detection system120 may determine a historical average value (e.g., intensity and/orrange) for each of multiple locations on the fault detection target 126,while in other embodiments the fault detection system 120 may determinean overall historical average value (e.g., intensity and/or range) forthe entire fault detection target 126. As will be discussed below, asnew Lidar data is received, the fault detection system 120 may updatethe historical Lidar data, for example, by updating one or more averagevalues.

The operation 410 may, in some embodiments, be performed as part of theoperation 320 where the fault detection system 120 detects a faultcondition occurring at the Lidar system 118. At operation 410, the faultdetection system 120 determines whether the difference between theincoming Lidar data and the historical Lidar data satisfies a thresholdcondition. The fault detection system 120 detects the fault conditionoccurring at the Lidar system 118 if the fault detection system 120determines that the difference satisfies the threshold condition. Forexample, the threshold condition may specify a threshold and the faultdetection system 120 may determine that the difference satisfies thethreshold condition if the difference exceeds the threshold. In a morespecific example, the fault detection system 120 may determine that adifference between an incoming range value and a historical range valueexceeds a threshold. As another example, the fault detection system 120may determine that a difference between an incoming intensity value anda historical intensity value exceeds a threshold.

The threshold condition may correspond to a pre-determined orconfigurable tolerance in differences between incoming and historicalLidar data. This tolerance may be set such that the differences betweenthe incoming and historical Lidar data do not impact the properfunctioning of the components of the AV system 100.

If the fault detection system 120 determines that the difference doesnot satisfy the threshold condition, the fault detection system 120updates the historical Lidar data using the incoming Lidar data, atoperation 415. In some embodiments, the fault detection system 120updates the historical Lidar data by adding one or more data points fromthe incoming Lidar data to the historical Lidar data or replacing one ormore data points in the historical Lidar data with one or more datapoints from the incoming Lidar data. In some embodiments, the faultdetection system 120 updates the historical Lidar data by recalculatingone or more average values based on data points from the incoming Lidardata. Further, based on a determination at operation 410 that thedifference does not satisfy the threshold condition, the AV system 100continues in a normal operation mode (at operation 420).

The method 300 as described above may, in some embodiments, be repeatedon a continuous basis such that the fault detection system 120 analyzesthe incoming Lidar data at each pass to detect fault conditions andupdates the historical Lidar data with the incoming Lidar data if nofault is detected. In some embodiments, the method 300 may beprogrammatically triggered. In some embodiments, the method 300 may berepeated at periodic time intervals (e.g., every 30 seconds).

Consistent with the embodiments described above in reference to FIG. 4 ,the historical data average may be used to detect a sudden change.However, in some embodiments, it may be undesirable to update thehistorical data average because gradual degradations in incoming Lidardata would slowly lead to a lower historical data average, which mayresult in the fault detection system 120 ignoring potential faultconditions. Accordingly, in some embodiments, historical Lidar data isstored when the AV system 100 is first qualified and commissioned. Ifthere is a later degradation in the operation of the Lidar system 118,the fault detection system 120 may detect a fault condition based on theinitial historical Lidar data.

In some embodiments, the method 300 may be triggered by certain externalevents or operations of the AV system 100. For example, as shown in FIG.5 , the method 300 may, in some embodiments, include operations 505,510, and 515. Consistent with these embodiments, the operations 505 and510 may be performed prior to operation 305, where the fault detectionsystem 120 obtains the incoming Lidar data. At operation 505, the faultdetection system 120 detects that the vehicle has stopped at a stoppinglocation. The stopping location may, for example, be a location wherethe AV stops to pick up or drop off one or more passengers, one or morepieces of cargo, or an item or a location where the AV stops because ofa traffic signal, stop sign, or other roadway feature. In some exampleswhere the AV is used to provide a ride service for passengers, thestopping location can be a place where the AV picks up or drops off apassenger. In other examples where the AV is used to provide a deliveryservice of food or other purchased items, the stopping location can be aplace where the AV parks to pick up an item or items for delivery or aplace where the AV makes a delivery of an item or items to a customer.Non-limiting examples of stopping locations include intersections,parking spots, driveways, roadway shoulders, and loading docks.

The fault detection system 120 may detect the vehicle being stopped atthe stopping location based on motion sensor data, Global PositioningSystem (GPS) data, sensor data provided by the sensors 104, other dataprovided by one or more components of the vehicle computing system 102(e.g., IMUs), or a combination thereof. In some embodiments, theoperations 315, 320, and 325 are performed in response to detecting thatthe vehicle has stopped at the stopping location.

In embodiments in which the fault detection target 126 includes anactuatable component that can be raised and lowered, the fault detectionsystem 120 actuates the component so that it becomes raised (atoperation 510) and is visible by each of the channels 200-0 to 200-N ofthe Lidar system 118. Accordingly, at operation 305, the incoming Lidardata obtained by the fault detection target 126 includes one or moredata points corresponding to the raised component.

Consistent with these embodiments, the operation 515 may be performed aspart of (e.g., a sub-routine or sub-task of) operation 325 where thefault detection system 120 initiates a fail-safe state at the AV system100. At operation 515, the fault detection system 120 causes the AV toremain stopped at the stopping location. For example, the faultdetection system 120 may provide a command to the vehicle controller 114to remain stopped at the stopping location. The AV system 100 may remainstopped at the stopping location until a remediation action is performedto correct the fault condition or until further commands are provided tothe vehicle controller 114.

As described above, the fault detection system 120 may utilize the faultdetection target 126 to detect fault conditions occurring at the Lidarsystem 118. In addition to fault conditions occurring at the Lidarsystem 118, the fault detection system 120 may utilize the faultdetection target 126 to detect fault conditions occurring at othersensors 104, such as a misalignment of one or more sensors 104.As shownin FIG. 6 , the method 300 may, in some embodiments, further includeoperations 605, 610, and 615. Consistent with these embodiments, theoperations 605, 610, and 615 may be performed prior to or as part ofoperation 320 where the fault detection system 120 detects the faultcondition.

At operation 605, the fault detection system 120 accesses sensor dataproduced by one of the sensors 104. For example, the fault detectionsystem 120 may access image data produced by an image sensor (e.g., acamera).

At operation 610, the fault detection system 120 performs a comparisonof the sensor data with the historical Lidar data. In comparing the setsof data, the fault detection system 120 may correlate a location of thefault detection target 126 represented in the historical Lidar data witha location of the fault detection target 122 represented in the sensordata. For example, the fault detection system 120 may correlate thelocation of the fault detection target 126 in the historical Lidar datawith the location of the fault detection target 126 in the image data.As noted above, the fault detection target 126 may comprise one or morecontrasting shapes or patterns, which would facilitate this correlation.

At operation 615, the fault detection system 120 verifies the alignmentof the one of the sensors 104. If the locations of the fault detectiontarget 126 in both the historical Lidar data and the sensor datacorrespond within at least a predetermined margin of error, the faultdetection system 120 determines that the alignment of the sensor 104 iscorrect. Otherwise, the fault detection system 120 may determine thatthe sensor 104 is misaligned, thereby resulting in a fault conditionbeing detected at operation 320.

FIG. 7 illustrates a diagrammatic representation of a machine 700 in theform of a computer system within which a set of instructions may beexecuted for causing the machine 700 to perform any one or more of themethodologies discussed herein, according to an example embodiment.Specifically, FIG. 7 shows a diagrammatic representation of the machine700 in the example form of a computer system, within which instructions716 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 700 to perform any one ormore of the methodologies discussed herein may be executed. For example,the instructions 716 may cause the machine 700 to execute the method300. In this way, the instructions 716 transform a general,non-programmed machine into a particular machine 700, such as the faultdetection system 120 or the vehicle computing system 102, that isspecially configured to carry out the described and illustratedfunctions in the manner described herein. In alternative embodiments,the machine 700 operates as a standalone device or may be coupled (e.g.,networked) to other machines. In a networked deployment, the machine 700may operate in the capacity of a server machine or a client machine in aserver-client network environment, or as a peer machine in apeer-to-peer (or distributed) network environment. The machine 700 maycomprise, but not be limited to, a server computer, a client computer, apersonal computer (PC), a tablet computer, a laptop computer, a netbook,a smart phone, a mobile device, a network router, a network switch, anetwork bridge, or any machine capable of executing the instructions716, sequentially or otherwise, that specify actions to be taken by themachine 700. Further, while only a single machine 700 is illustrated,the term “machine” shall also be taken to include a collection ofmachines 700 that individually or jointly execute the instructions 716to perform any one or more of the methodologies discussed herein.

The machine 700 may include processors 710, memory 730, and input/output(I/O) components 750, which may be configured to communicate with eachother such as via a bus 702. In an example embodiment, the processors710 (e.g., a central processing unit (CPU), a reduced instruction setcomputing (RISC) processor, a complex instruction set computing (CISC)processor, a graphics processing unit (GPU), a digital signal processor(DSP), an application-specific integrated circuit (ASIC), aradio-frequency integrated circuit (RFIC), another processor, or anysuitable combination thereof) may include, for example, a processor 712and a processor 714 that may execute the instructions 716. The term“processor” is intended to include multi-core processors 710 that maycomprise two or more independent processors (sometimes referred to as“cores”) that may execute instructions contemporaneously. Although FIG.7 shows multiple processors 710, the machine 700 may include a singleprocessor with a single core, a single processor with multiple cores(e.g., a multi-core processor), multiple processors with a single core,multiple processors with multiple cores, or any combination thereof.

The memory 730 may include a main memory 732, a static memory 734, and astorage unit 736 comprising a machine storage medium 737, eachaccessible to the processors 710 such as via the bus 702. The mainmemory 732, the static memory 734, and the storage unit 736 store theinstructions 716 embodying any one or more of the methodologies orfunctions described herein. The instructions 716 may also reside,completely or partially, within the main memory 732, within the staticmemory 734, within the storage unit 736, within at least one of theprocessors 710 (e.g., within the processor’s cache memory), or anysuitable combination thereof, during execution thereof by the machine700.

The I/O components 750 may include components to receive input, provideoutput, produce output, transmit information, exchange information,capture measurements, and so on. The specific I/O components 750 thatare included in a particular machine 700 will depend on the type ofmachine. For example, portable machines such as mobile phones willlikely include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O components 750 mayinclude many other components that are not shown in FIG. 7 . The I/Ocomponents 750 are grouped according to functionality merely forsimplifying the following discussion, and the grouping is in no waylimiting. In various example embodiments, the I/O components 750 mayinclude output components 752 and input components 754. The outputcomponents 752 may include visual components (e.g., a display such as aplasma display panel (PDP), a light-emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), other signal generators, and soforth. The input components 754 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or another pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 750 may include communication components 764 operableto couple the machine 700 to a network 780 or devices 770 via a coupling782 and a coupling 772, respectively. For example, the communicationcomponents 764 may include a network interface component or anothersuitable device to interface with the network 780. In further examples,the communication components 764 may include wired communicationcomponents, wireless communication components, cellular communicationcomponents, and other communication components to provide communicationvia other modalities (e.g., Bluetooth, Wi-Fi, and near-fieldcommunication (NFC)). The devices 770 may be another machine or any of awide variety of peripheral devices (e.g., a peripheral device coupledvia a universal serial bus (USB)).

EXECUTABLE INSTRUCTIONS AND MACHINE-STORAGE MEDIUM

The various memories (e.g., 730, 732, 734, and/or memory of theprocessor(s) 710) and/or the storage unit 736 may store one or more setsof instructions 716 and data structures (e.g., software) embodying orutilized by any one or more of the methodologies or functions describedherein. These instructions, when executed by the processor(s) 710, causevarious operations to implement the disclosed embodiments.

As used herein, the terms “machine-storage medium,” “device-storagemedium,” and “computer-storage medium” mean the same thing and may beused interchangeably. The terms refer to a single or multiple storagedevices and/or media (e.g., a centralized or distributed database,and/or associated caches and servers) that store executable instructionsand/or data. The terms shall accordingly be taken to include, but not belimited to, solid-state memories, and optical and magnetic media,including memory internal or external to processors. Specific examplesof machine-storage media, computer-storage media, and/or device-storagemedia include non-volatile memory, including by way of examplesemiconductor memory devices, e.g., erasable programmable read-onlymemory (EPROM), electrically erasable programmable read-only memory(EEPROM), field-programmable gate arrays (FPGAs), and flash memorydevices; magnetic disks such as internal hard disks and removable disks;magneto-optical disks; and CD-ROM and DVD-ROM disks. The terms“machine-storage media,” “computer-storage media,” and “device-storagemedia” specifically exclude carrier waves, modulated data signals, andother such media, at least some of which are covered under the term“transmission medium” discussed below.

TRANSMISSION MEDIUM

In various example embodiments, one or more portions of the network 780may be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local-area network (LAN), a wireless LAN (WLAN), awide-area network (WAN), a wireless WAN (WWAN), a metropolitan-areanetwork (MAN), the Internet, a portion of the Internet, a portion of thepublic switched telephone network (PSTN), a plain old telephone service(POTS) network, a cellular telephone network, a wireless network, aWi-Fi® network, another type of network, or a combination of two or moresuch networks. For example, the network 780 or a portion of the network780 may include a wireless or cellular network, and the coupling 782 maybe a Code Division Multiple Access (CDMA) connection, a Global Systemfor Mobile communications (GSM) connection, or another type of cellularor wireless coupling. In this example, the coupling 782 may implementany of a variety of types of data transfer technology, such as SingleCarrier Radio Transmission Technology (1xRTT), Evolution-Data Optimized(EVDO) technology, General Packet Radio Service (GPRS) technology,Enhanced Data rates for GSM Evolution (EDGE) technology, thirdGeneration Partnership Project (3GPP) including 3G, fourth generationwireless (4G) networks, Universal Mobile Telecommunications System(UMTS), High-Speed Packet Access (HSPA), Worldwide Interoperability forMicrowave Access (WiMAX), Long-Term Evolution (LTE) standard, othersdefined by various standard-setting organizations, other long-rangeprotocols, or other data transfer technology.

The instructions 716 may be transmitted or received over the network 780using a transmission medium via a network interface device (e.g., anetwork interface component included in the communication components764) and utilizing any one of a number of well-known transfer protocols(e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions716 may be transmitted or received using a transmission medium via thecoupling 772 (e.g., a peer-to-peer coupling) to the devices 770. Theterms “transmission medium” and “signal medium” mean the same thing andmay be used interchangeably in this disclosure. The terms “transmissionmedium” and “signal medium” shall be taken to include any intangiblemedium that is capable of storing, encoding, or carrying theinstructions 716 for execution by the machine 700, and include digitalor analog communications signals or other intangible media to facilitatecommunication of such software. Hence, the terms “transmission medium”and “signal medium” shall be taken to include any form of modulated datasignal, carrier wave, and so forth. The term “modulated data signal”means a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in the signal.

COMPUTER-READABLE MEDIUM

The terms “machine-readable medium,” “computer-readable medium,” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms are defined to includeboth machine-storage media and transmission media. Thus, the termsinclude both storage devices/media and carrier waves/modulated datasignals.

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Similarly, the methods described hereinmay be at least partially processor-implemented. For example, at leastsome of the operations of a method may be performed by one or moreprocessors. The performance of certain of the operations may bedistributed among the one or more processors, not only residing within asingle machine, but deployed across a number of machines. In someexample embodiments, the processor or processors may be located in asingle location (e.g., within a home environment, an office environment,or a server farm), while in other embodiments the processors may bedistributed across a number of locations.

Although the embodiments of the present disclosure have been describedwith reference to specific example embodiments, it will be evident thatvarious modifications and changes may be made to these embodimentswithout departing from the broader scope of the inventive subjectmatter. Accordingly, the specification and drawings are to be regardedin an illustrative rather than a restrictive sense. The accompanyingdrawings that form a part hereof show, by way of illustration, and notof limitation, specific embodiments in which the subject matter may bepracticed. The embodiments illustrated are described in sufficientdetail to enable those skilled in the art to practice the teachingsdisclosed herein. Other embodiments may be used and derived therefrom,such that structural and logical substitutions and changes may be madewithout departing from the scope of this disclosure. This DetailedDescription, therefore, is not to be taken in a limiting sense, and thescope of various embodiments is defined only by the appended claims,along with the full range of equivalents to which such claims areentitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent, to those of skill inthe art, upon reviewing the above description.

In this document, the terms “a” and “an” are used, as is common inpatent documents, to include one or more than one, independent of anyother instances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. In the appended claims, the terms “including” and“in which” are used as the plain-English equivalents of the respectiveterms “comprising” and “wherein.” Also, in the following claims, theterms “including” and “comprising” are open-ended; that is, a system,device, article, or process that includes elements in addition to thoselisted after such a term in a claim is still deemed to fall within thescope of that claim.

1-20. (canceled)
 21. An autonomous vehicle (AV) system for an autonomousvehicle, the AV system comprising: a Light Detection and Ranging (Lidar)system comprising a plurality of channels, the Lidar system configuredto output Lidar data indicative of objects reflecting laser signalsemitted by the Lidar system; one or more detection features on anexterior of the autonomous vehicle; and one or more processors toperform operations comprising: obtaining incoming Lidar data output bythe Lidar system during operation of the AV system, the incoming Lidardata comprising at least one Lidar point corresponding to the one ormore detection features; obtaining, from a memory, historical Lidar databased on previous output of the Lidar system, the historical Lidar datacomprising at least one historical Lidar point corresponding to the oneor more detection features from the previous output of the Lidar system,the at least one historical Lidar point comprising at least one of ahistorical range value or a historical intensity value; determining acondition at the Lidar system based on incoming data points of theincoming Lidar data with historical data points of the historical Lidardata; and initiating a restricted-operation state based on determiningthe condition.
 22. The AV system of claim 21, wherein determining thecondition at the Lidar system comprises: performing a comparison of theincoming data points of the incoming Lidar data with the historical datapoints of the historical Lidar data; and determining a differencebetween the incoming Lidar data and the historical Lidar data based onthe comparison.
 23. The AV system of claim 22, wherein determining thecondition further comprises: determining that the difference between theincoming Lidar data and the historical Lidar data satisfies a thresholdcondition.
 24. The AV system of claim 22, wherein the differencecomprises one of: a difference between the historical intensity valueand an incoming intensity value; or a difference between the historicalrange value and an incoming range value.
 25. The AV system of claim 21,wherein one or more operations of the AV system are restricted while theAV system is in the restricted-operation state.
 26. The AV system ofclaim 25, wherein the restricted-operation state comprises stopping theautonomous vehicle at an available stopping location.
 27. The AV systemof claim 21, wherein the initiating of the restricted-operation statecomprises providing one or more commands to the autonomous vehicle. 28.The AV system of claim 21, wherein the one or more detection featurescomprise a coating applied to the exterior of the autonomous vehicle.29. The AV system of claim 21, wherein the one or more detectionfeatures comprise one or more markings that are visible within a fieldof view of the Lidar system.
 30. The AV system of claim 21, wherein theoperations further comprise: determining that the autonomous vehicle isstopped at a location, wherein the one or more processors obtain theincoming Lidar data in response to determining that the autonomousvehicle is stopped at the location.
 31. The AV system of claim 30,wherein the initiating of the restricted-operation state comprises:causing the autonomous vehicle to remain stopped at the location until aremediation action is performed.
 32. The AV system of claim 30, wherein:the one or more detection features further comprise an actuatablecomponent; and the AV system actuates the actuatable component inresponse to determining that the autonomous vehicle is stopped.
 33. TheAV system of claim 21, wherein the condition includes one or more of: adamaged component, a dirty component, a dislodged component, a reducedor diminished intensity of laser signals emitted by the Lidar system, ora reduced sensitivity of a detector of the Lidar system.
 34. A methodcomprising: obtaining incoming Light Detection and Ranging (Lidar) dataoutput by a Lidar system during operation of an autonomous vehicle, theLidar system comprising a plurality of channels and being configured tooutput Lidar data indicative of objects reflecting laser signals emittedby the Lidar system, the incoming Lidar data corresponding to one ormore detection features on an exterior of the autonomous vehicle;obtaining, from a memory, historical Lidar data based on previous outputof the Lidar system, the historical Lidar data corresponding to the oneor more detection features and comprising at least one of a historicalrange value or a historical intensity value; determining a condition atthe Lidar system based on the incoming Lidar data and the historicalLidar data; and initiating a restricted-operation state for theautonomous vehicle based on determining the condition.
 35. The method ofclaim 34, wherein determining the condition comprises: performing acomparison of the incoming Lidar data with the historical Lidar data;based on the comparison, determining a difference between the incomingLidar data and the historical Lidar data; and determining that thedifference between the incoming Lidar data and the historical Lidar datasatisfies a threshold condition.
 36. The method of claim 35, wherein thedifference comprises one of: a difference between the historicalintensity value and an incoming intensity value; or a difference betweenthe historical range value and an incoming range value.
 37. The methodof claim 34, wherein one or more operations of the autonomous vehicleare restricted while the autonomous vehicle is in therestricted-operation state.
 38. The method of claim 34, furthercomprising: determining that the autonomous vehicle is stopped at alocation, wherein the obtaining of the incoming Lidar data is inresponse to determining that the autonomous vehicle is stopped at thelocation; and causing the autonomous vehicle to remain stopped at thelocation.
 39. The method of claim 38, wherein: the one or more detectionfeatures further comprise an actuatable component; and the methodfurther comprises actuating the actuatable component in response todetermining that the autonomous vehicle is stopped.
 40. A machinestorage medium storing instructions that, when executed by one or moreprocessors, cause the one or more processors to perform operationscomprising: obtaining incoming Light Detection and Ranging (Lidar) dataoutput by a Lidar system during operation of an autonomous vehicle, theLidar system comprising a plurality of channels and being configured tooutput Lidar data indicative of objects reflecting laser signals emittedby the Lidar system, the incoming Lidar data corresponding to one ormore detection features on an exterior of the autonomous vehicle;obtaining, from a memory, historical Lidar data based on previous outputof the Lidar system, the historical Lidar data corresponding to the oneor more detection features, the historical Lidar data comprising atleast one of a historical range value or a historical intensity value;determining a condition at the Lidar system based on the incoming Lidardata and the historical Lidar data; and initiating arestricted-operation state at the AV system based on determining thecondition at the Lidar system.